Assessment of a Beamforming Implementation Developed for Surface Sound Source Separation

This paper presents the assessment of a scan-and-sum beamformer by numerical simulations. The scan-and-sum beamformer has been proposed and analyzed theoretically, in concern with sound source separation of general wide band surface sources distributed in the azimuth angle dimension. Sound sources emitted by regions are called surface sources, and tend to have various shapes and sizes, e.g., a waterfall or an orchestra on the stage. Conventionally a sound source is modelled as a point source that is without a shape or size, hence conventional beamformers are mainly designed for point source separation. A scan-and-sum beamformer deploys a conventional beamformer as the sub-beamformer, and scans the region where a target surface source exists at an appropriate scanning density. The separated surface source is formed through a weighted summation of sub-beamformers. Implementations based on the MVDR sub-beamformer are presented under a framework that reduces overlapping calculation of inverse correlation matrices. For inverse correlation estimation, two methods are provided, one is a block-wise processing which further reduces computational cost, and the other is RLS-based inverse matrix calculation which displays strength in accuracy of estimation. A self-designed diverse dataset having various mixtures of surface sound sources is also created to carry out extensive numerical simulations for a detailed comparison between the scan-and-sum beamformer method and the conventional MVDR approach. Simulations validated the efficiency and effectiveness of the current implementation, and showed that the proposed scan-and-sum beamformer outperforms a conventional one in surface sound source separation.